This study proposes a motion planning and control system based on collision risk potential predictioncharacteristics of experienced drivers. Recently, automatic braking systems have been deployed in current automotive markets. However, the existing systems cannot avoid collisions in critical scenario such as a pedestrian suddenly darting out from a poor-visibility blind corner. By optimizing the potential field function in the framework of optimal control theory, the desired yaw rate and the desired longitudinal deceleration are theoretically calculated. Finally, the validity of the proposed motion planning and control system is verified by comparing the simulation results with the actual driving data by experienced drivers.

This study proposes an autonomous obstacle avoidance system not only by braking but also by steering, as one of the active safety technologies to prevent traffic accidents. The proposed system prevents the vehicle from colliding with a moving obstacle like a pedestrian jumping out from the roadside. In the proposed system, to avoid the predicted colliding position based on constant-velocity obstacle motion assumption, the avoidance trajectory is derived as connected two identical arcs. The system then controls the vehicle autonomously by the combined control of the braking and steering systems. In this paper, the proposed system is examined by real car experiments and its effectiveness is shown from the results of the experiments.

This article presents the alternative lane keeping control strategy with direct yaw moment control (DYC) that utilizes the transverse driving torque distribution. With the sophisticated structure of today’s electric vehicle, the small-size motor can be placed into each driving wheel so that the DYC input can be easily and precisely achieved by controlling the in-wheel-motors independently. The information of road position is acquired by vision system via CCD camera and on-board image processing system. As a feature of the proposed system, the lateral deviation detected by CCD camera is converted into the desired yaw rate for tracing the desired lane. Then, the yaw moment control input is theoretically determined to achieve the desired yaw rate from the viewpoint of vehicle dynamics. This article presents the theoretical analysis of lane keeping control characteristics using linear vehicle model on planar motion, and experimental study using micro-scale electric vehicle to verify the effectiveness of the proposed strategy.

This paper proposes a direct yaw moment control (DYC) algorithm based on the recognition of driver steering intention. To reflect the driver steering behaviour on DYC algorithm, two types of desired yaw rates are proposed. One is for the regulation of lateral deviation as lane-keeping function and the other is for the regulation of side-slip angle as vehicle stability control. In the control algorithm, two types of desired yaw rates are switched by a weighting coefficient according to the driver steering behaviour, which is recognised with the application of the Hidden Markov Model. Finally, the effectiveness of recognition algorithm is verified and the effectiveness of the proposed DYC system on driver–vehicle system is proved by using experimental vehicle.

The collision avoidance (CA) system is a pivotal part of the autonomous vehicle. Ability to navigate the vehicle in various hazardous scenarios demands reliable actuator interventions. In a complex CA scenario, the increased nonlinearity requires a dependable control strategy. For example, during collisions with a sudden appearing obstacle (i.e. crossing pedestrian, vehicle), the abrupt increment of vehicle longitudinal and lateral forces summation during the CA maneuver demands a system with the ability to handle coupled nonlinear dynamics. Failure to address the aforementioned issues will result in collisions and near-miss incidents. Thus, to solve these issues, a nonlinear model predictive control (NMPC)-based path tracking strategy is proposed as the automated motion guidance for the host vehicle CA architecture. The system is integrated with the artificial potential field (APF) as the motion planning strategy. In a hazardous scenario, APF measures the collision risks and formulates the desired yaw rate and deceleration metrics for the path replanning. APF ensures an optimal replanned trajectory by including the vehicle dynamics into its optimization formulation. NMPC then acts as the coupled path and speed tracking controller to enable vehicle navigation. To accommodate vehicle comfort during the avoidance, NMPC is constrained. Due to its complexity as a nonlinear controller, NMPC can be time-consuming. Therefore, a move blocking strategy is assimilated within the architecture to decrease the system’s computational burden. The modular nature of the architecture allows each strategy to be tuned and developed independently without affecting each others’ performance. The system’s tracking performance is analyzed by computational simulations with several CA scenarios (crossing pedestrian, parked bus, and sudden appearing moving vehicle at an intersection). NMPC tracking performance is compared to the nominal MPC and linear controllers. The effect of move blocking strategies on NMPC performance are analyzed, and the results are compared in terms of mean squared error values. The inclusion of nonlinear tracking controllers in the architecture is shown to provide reliable CA actions in various hazardous scenarios. The work is important for the development of a reliable controller strategy for multi-scenario CA of the fully autonomous vehicle.

SUMMARY Alternative methods of automatic steering system are investigated by using a vehicle-mounted camera that detects lane markers ahead of the vehicle. The control input of such a steering system can be classified into steering angle and steering torque. This paper investigates the physical characteristics of automatic lane-tracking controller using steering angle and steering torque as the control input, and compares their differences. Generally, most of researches about lane-tracking control manoeuvre deal with steering angle control method due to its excellent robustness. In contrast, the steering torque control provides some degree of freedom in permitting the driver to steer the vehicle, thus it has potential for development as a steering assist mechanism. In this paper, the controller design is Linear Quadratic (LQ) controller based on the simplified dynamics of a 2 degree-of-freedom bicycle model and simplified steering model.